Optimizing without knowing the input

Some of the coolest questions in our field are about optimization over input
that is kept by selfish agents ("mechanism design"),
depends on future events ("online algorithms"),
or has to be reconstructed from noisy samples ("machine learning").

Eric Balkanski, Aviad Rubinstein, Yaron Singer: "The Limitations of Optimization from Samples".
STOC 2017 (arXiv)
See also this related paper from NIPS 2016.

Complexity of Nash equilibrium

This is a favorite meta-question in the intersection of the previous two themes.

Yakov Babichenko, Aviad Rubinstein: “Communication complexity of approximate Nash equilibria”.
STOC 2017 (arXiv) Invited to special issue of GEB.
See also this really nice Quanta Magazinearticle about our work,
by Erica Klarreich.
See also this paper with Mika Goos (FOCS 2018) where we obtain near-tight bounds.